By the end of this course, learners are provided a high-level overview of data analysis and visualization tools, and are prepared to discuss best practices and develop an ensuing action plan that addresses key discoveries. It begins with common hurdles that obstruct adoption of a data-driven culture before introducing data analysis tools (R software, Minitab, MATLAB, and Python). Deeper examination is spent on statistical process control (SPC), which is a method for studying variation over time. The course also addresses do’s and don’ts of presenting data visually, visualization software (Tableau, Excel, Power BI), and creating a data story.
Data Analysis and Visualization
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Data Analysis and Visualization
This course is part of Data-Driven Decision Making (DDDM) Specialization
Instructors: Peter Baumgartner
20,866 already enrolled
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172 reviews
Beginner level
Recommended experience
Flexible schedule
1 week at 10 hours a week
Learn at your own pace
94%
Most learners liked this course
What you'll learn
Identify stakeholders and key components imperative to an analytics project plan
Name strengths and weaknesses of different analysis and visualization tools
Visually identify, monitor, and remove process variation
Explain how to create a compelling data story
Skills you'll gain
- Data Analysis
- Action Oriented
- Statistical Process Controls
- Data-Driven Decision-Making
- Business Analytics
- Data Visualization Software
- Data Storytelling
- Data Analysis Software
- Statistical Analysis
- Variance Analysis
- Data Cleansing
- Process Analysis
- Data Quality
- Process Capability
- Tableau Software
- Data Presentation
- Business Planning
Tools you'll learn
Details to know
Shareable certificate
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Taught in English
91%
of learners achieved a positive career outcome
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This course is part of the Data-Driven Decision Making (DDDM) Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
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Instructor ratings
(56 ratings)
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University of Colorado Boulder
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Showing 3 of 172
VG
Reviewed on Dec 15, 2020
Examples and better instructions on the assignment would improve this course.
OM
Reviewed on Jul 11, 2022
A very educative course indeed. Thanks to Coursera and its multiple partners who made this possible. lam forever grateful for being afforded such an opportunity. Thanks to the course facilitators.
VN
Reviewed on Apr 24, 2025
The graded assignments should be more related to the content. This course was on visualization, but the assignment was not fully related to the modules.
¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.